Model-based quantification of metabolic interactions from dynamic microbial-community data
Model-based quantification of metabolic interactions from dynamic microbial-community data
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DOI：
10.1371/journal.pone.0173183
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发表时间：
2017-03-09
期刊：
影响因子：
3.7
通讯作者：
Teusink, Bas
中科院分区：
文献类型：
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作者：
Hanemaaijer, Mark;Olivier, Brett G.;Teusink, Bas
微生物生态学的一个重要挑战是从群落水平的数据中推断出生长中的微生物物种之间的代谢交换通量，涉及物种丰度和代谢物浓度。在这里，我们应用基于模型的方法来整合这些实验数据，从而推断代谢交换通量。我们设计了一种合成的厌氧共培养物的丙酮丁醇梭菌和Wolinella succinogenes相互作用，通过种间氢转移和应用不同的环境条件，我们预计代谢交换率的变化。我们使用了两种微生物代谢的化学计量模型，这代表了我们目前的生理理解，并发现这种理解-模型-足以推断代谢交换通量的身份和大小，并提出了意想不到的相互作用。当模型不能拟合所有实验数据时，表明了进一步生理研究的具体要求。结果表明，氮源对共培养体系中的间质氢转移速率有影响。此外，该模型可以预测细胞内通量和最佳代谢交换率，这可以指向工程策略。因此，这项研究提供了一个现实的说明的优势和劣势的模型为基础的集成异构数据，使推断代谢交换通量可能从社区水平的实验数据。
An important challenge in microbial ecology is to infer metabolic-exchange fluxes between growing microbial species from community-level data, concerning species abundances and metabolite concentrations. Here we apply a model-based approach to integrate such experi-mental data and thereby infer metabolic-exchange fluxes. We designed a synthetic anaero-bic co-culture of Clostridium acetobutylicum and Wolinella succinogenes that interact via interspecies hydrogen transfer and applied different environmental conditions for which we expected the metabolic-exchange rates to change. We used stoichiometric models of the metabolism of the two microorganisms that represents our current physiological under-standing and found that this understanding -the model -is sufficient to infer the identity and magnitude of the metabolic-exchange fluxes and it suggested unexpected interactions. Where the model could not fit all experimental data, it indicates specific requirement for fur-ther physiological studies. We show that the nitrogen source influences the rate of interspe-cies hydrogen transfer in the co-culture. Additionally, the model can predict the intracellular fluxes and optimal metabolic exchange rates, which can point to engineering strategies. This study therefore offers a realistic illustration of the strengths and weaknesses of model-based integration of heterogenous data that makes inference of metabolic-exchange fluxes possible from community-level experimental data.